Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Biosignal Sensing and Processing for Clinical Diagnosis

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 37467

Share This Special Issue

Special Issue Editors

Dr. Gema Prats-Boluda

E-Mail Website
Guest Editor

Centro de investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 València, Spain
Interests: biosignal sensing and processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Javier Garcia-Casado

E-Mail Website
Guest Editor

Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: biomedical engineering; signal processing; signal instrumentation; monitoring devices
Special Issues, Collections and Topics in MDPI journals

Dr. Yvonne Tran

E-Mail Website
Guest Editor

Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
Interests: biomedical signals; psychophysiology; injury; electroencephalography; heart rate variability; machine learning for rehabilitation medicine
Special Issues, Collections and Topics in MDPI journals

Dr. Yiyao Ye-Lin

E-Mail Website
Guest Editor

Dr. José Luis Martinez de Juan

E-Mail Website
Guest Editor

Centro de investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 València, Spain
Interests: biosignal sensing and processing
Special Issues, Collections and Topics in MDPI journals

Dr. Dongmei Hao

E-Mail Website
Guest Editor

College of Life Science and Bioengineering, Beijing University of Technology (BJUT), Beijing 100124, China
Interests: physiological signal measurement and analysis; medical instrument development and medical pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biosignals have a long history of use in the clinical diagnosis and follow-up of multiple pathologies, as is the case of the electrocardiogram in cardiology. Advances in sensing and computing, as well as the emergence of artificial intelligence, have driven a great advance in this field, expanding the diagnostic spectrum of traditionally used biosignals, improving biosignal quality, and opening the door to the use of new biosignals such as biochemical or biomagnetic signals. Obtaining valuable and clinically useful information is still challenging, involving the development and/or selection of the appropriate biosignal sensing systems and algorithms for automatic signal segmentation, denoising or artifact removal, signal parameterization, and feature selection. Recently, the challenge of obtaining information useful for clinical diagnosis has been addressed by the development of decision support systems via machine or deep learning. It is important to consider all of these developments in the context of their implementation in clinically friendly systems that minimize patient discomfort, are simple to use, and provide information that is easily interpretable by physicians in near real time.

The aim of the Special Issue “Biosignal Sensing and Processing for Clinical Diagnosis” is to collect a compendium of articles about new trends and advances in biosignal sensing and processing as well as their use in clinical decision support systems. We look forward to your participation in this Special Issue. Topics of interest include, but are not limited to, the following:

New trends in biosignal sensing;
Biosignal processing and analysis: electrocardiographic, myoelectric, electroencephalographic, photoplethysmographic, gastric, biochemical, or biomagnetic signals, among others;
Applications of machine learning, deep learning, and artificial intelligence in using biosignals for clinical diagnosis.

Dr. Gema Prats Boluda
Dr. Javier Garcia-Casado
Dr. Yvonne Tran
Dr. Yiyao Ye-Lin
Dr. José Luis Martinez de Juan
Dr. Dongmei Hao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

biosignals
biomedical sensors
biomedical signal
signal processing
feature extraction
machine learning
deep learning

Published Papers (13 papers)

Download All Papers

Research

12 pages, 3158 KiB

Open AccessArticle

An Efficient Hybrid Methodology for Local Activation Waves Detection under Complex Fractionated Atrial Electrograms of Atrial Fibrillation

by Diego Osorio, Aikaterini Vraka, Aurelio Quesada, Fernando Hornero, Raúl Alcaraz and José J. Rieta

Sensors 2022, 22(14), 5345; https://doi.org/10.3390/s22145345 - 18 Jul 2022

Cited by 2 | Viewed by 1316

Abstract

Local activation waves (LAWs) detection in complex fractionated atrial electrograms (CFAEs) during catheter ablation (CA) of atrial fibrillation (AF), the commonest cardiac arrhythmia, is a complicated task due to their extreme variability and heterogeneity in amplitude and morphology. There are few published works on reliable LAWs detectors, which are efficient for regular or low fractionated bipolar electrograms (EGMs) but lack satisfactory results when CFAEs are analyzed. The aim of the present work is the development of a novel optimized method for LAWs detection in CFAEs in order to assist cardiac mapping and catheter ablation (CA) guidance. The database consists of 119 bipolar EGMs classified by AF types according to Wells’ classification. The proposed method introduces an alternative Botteron’s preprocessing technique targeting the slow and small-ampitude activations. The lower band-pass filter cut-off frequency is modified to 20 Hz, and a hyperbolic tangent function is applied over CFAEs. Detection is firstly performed through an amplitude-based threshold and an escalating cycle-length (CL) analysis. Activation time is calculated at each LAW’s barycenter. Analysis is applied in five-second overlapping segments. LAWs were manually annotated by two experts and compared with algorithm-annotated LAWs. AF types I and II showed

100 %

accuracy and sensitivity. AF type III showed

92.77 %

accuracy and

95.30 %

sensitivity. The results of this study highlight the efficiency of the developed method in precisely detecting LAWs in CFAEs. Hence, it could be implemented on real-time mapping devices and used during CA, providing robust detection results regardless of the fractionation degree of the analyzed recordings. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

18 pages, 3051 KiB

Open AccessArticle

Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System

by Roberto Zazo-Manzaneque, Vicente Pons-Beltrán, Ana Vidaurre, Alberto Santonja and Carlos Sánchez-Díaz

Sensors 2022, 22(14), 5211; https://doi.org/10.3390/s22145211 - 12 Jul 2022

Viewed by 1355

Abstract

Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For its correct operation, it is essential to detect in real time if the inflatable cavities are malfunctioning (presence of air leakage). Two classification predictive models were obtained, one for each cavity typology, which must discern between the “Right” or “Leak” states. The cavity pressure signals were digitally processed, from which a set of features were extracted and selected. The predictive models were obtained from the features, and a prior classification of the signals between the two possible states was used as input to different supervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the balloon-type was 99.62%, while that of the bellows-type was 100%, representing an encouraging result. Once the models are validated with data generated in animal model tests and subsequently in exploratory clinical tests, their incorporation in the software device will ensure patient safety during small bowel exploration. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

24 pages, 51747 KiB

Open AccessArticle

Interpretable Assessment of ST-Segment Deviation in ECG Time Series

by Israel Campero Jurado, Andrejs Fedjajevs, Joaquin Vanschoren and Aarnout Brombacher

Sensors 2022, 22(13), 4919; https://doi.org/10.3390/s22134919 - 29 Jun 2022

Cited by 3 | Viewed by 2197

Abstract

Nowadays, even with all the tremendous advances in medicine and health protocols, cardiovascular diseases (CVD) continue to be one of the major causes of death. In the present work, we focus on a specific abnormality: ST-segment deviation, which occurs regularly in high-performance athletes and elderly people, serving as a myocardial infarction (MI) indicator. It is usually diagnosed manually by experts, through visual interpretation of the printed electrocardiography (ECG) signal. We propose a methodology to detect ST-segment deviation and quantify its scale up to 1 mV by extracting statistical, point-to-point beat characteristics and signal quality indexes (SQIs) from single-lead ECG. We do so by applying automated machine learning methods to find the best hyperparameter configuration for classification and regression models. For validation of our method, we use the ST-T database from Physionet; the results show that our method obtains 98.30% accuracy in the case of a multiclass problem and 99.87% accuracy in the case of binarization. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

29 pages, 2762 KiB

Open AccessArticle

Assessment of a Passive Lumbar Exoskeleton in Material Manual Handling Tasks under Laboratory Conditions

by Sofía Iranzo, Alicia Piedrabuena, Fernando García-Torres, Jose Luis Martinez-de-Juan, Gema Prats-Boluda, Mercedes Sanchis and Juan-Manuel Belda-Lois

Sensors 2022, 22(11), 4060; https://doi.org/10.3390/s22114060 - 27 May 2022

Cited by 5 | Viewed by 2138

Abstract

Manual material handling tasks in industry cause work-related musculoskeletal disorders. Exoskeletons are being introduced to reduce the risk of musculoskeletal injuries. This study investigated the effect of using a passive lumbar exoskeleton in terms of moderate ergonomic risk. Eight participants were monitored by electromyogram (EMG) and motion capture (MoCap) while performing tasks with and without the lumbar exoskeleton. The results showed a significant reduction in the root mean square (VRMS) for all muscles tracked: erector spinae (

8 %

), semitendinosus (

14 %

), gluteus (

5 %

), and quadriceps (

10.2 %

). The classic fatigue parameters showed a significant reduction in the case of the semitendinosus:

1.7 %

zero-crossing rate,

0.9 %

mean frequency, and

1.12 %

median frequency. In addition, the logarithm of the normalized Dimitrov’s index showed reductions of 11.5, 8, and 14% in erector spinae, semitendinosus, and gluteus, respectively. The calculation of range of motion in the relevant joints demonstrated significant differences, but in almost all cases, the differences were smaller than

10 %

. The findings of the study indicate that the passive exoskeleton reduces muscle activity and introduces some changes of strategies for motion. Thus, EMG and MoCap appear to be appropriate measurements for designing an exoskeleton assessment procedure. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

19 pages, 981 KiB

Open AccessArticle

Towards a Non-Contact Method for Identifying Stress Using Remote Photoplethysmography in Academic Environments

by Hector Manuel Morales-Fajardo, Jorge Rodríguez-Arce, Alejandro Gutiérrez-Cedeño, José Caballero Viñas, José Javier Reyes-Lagos, Eric Alonso Abarca-Castro, Claudia Ivette Ledesma-Ramírez and Adriana H. Vilchis-González

Sensors 2022, 22(10), 3780; https://doi.org/10.3390/s22103780 - 16 May 2022

Cited by 6 | Viewed by 3425

Abstract

Stress has become a common condition and is one of the chief causes of university course disenrollment. Most of the studies and tests on academic stress have been conducted in research labs or controlled environments, but these tests can not be extended to a real academic environment due to their complexity. Academic stress presents different associated symptoms, anxiety being one of the most common. This study focuses on anxiety derived from academic activities. This study aims to validate the following hypothesis: by using a non-contact method based on the use of remote photoplethysmography (rPPG), it is possible to identify academic stress levels with an accuracy greater than or equal to that of previous works which used contact methods. rPPG signals from 56 first-year engineering undergraduate students were recorded during an experimental task. The results show that the rPPG signals combined with students’ demographic data and psychological scales (the State–Trait Anxiety Inventory) improve the accuracy of different classification methods. Moreover, the results demonstrate that the proposed method provides 96% accuracy by using K-nearest neighbors, J48, and random forest classifiers. The performance metrics show better or equal accuracy compared to other contact methods. In general, this study demonstrates that it is possible to implement a low-cost method for identifying academic stress levels in educational environments. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

18 pages, 4517 KiB

Open AccessArticle

Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors

by Yajun Zhang, Dongmei Hao, Lin Yang, Xiya Zhou, Yiyao Ye-Lin and Yimin Yang

Sensors 2022, 22(9), 3352; https://doi.org/10.3390/s22093352 - 27 Apr 2022

Cited by 4 | Viewed by 1679

Abstract

Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) ≤ 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD ≤ 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

12 pages, 2769 KiB

Open AccessArticle

Evaluation of VDT-Induced Visual Fatigue by Automatic Detection of Blink Features

by Zhijie Yin, Bing Liu, Dongmei Hao, Lin Yang and Yongkang Feng

Sensors 2022, 22(3), 916; https://doi.org/10.3390/s22030916 - 25 Jan 2022

Cited by 4 | Viewed by 2802

Abstract

This study evaluates the progression of visual fatigue induced by visual display terminal (VDT) using automatically detected blink features. A total of 23 subjects were recruited to participate in a VDT task, during which they were required to watch a 120-min video on a laptop and answer a questionnaire every 30 min. Face video recordings were captured by a camera. The blinking and incomplete blinking images were recognized by automatic detection of the parameters of the eyes. Then, the blink features were extracted including blink number (BN), mean blink interval (Mean_BI), mean blink duration (Mean_BD), group blink number (GBN), mean group blink interval (Mean_GBI), incomplete blink number (IBN), and mean incomplete blink interval (Mean_IBI). The results showed that BN and GBN increased significantly, and that Mean_BI and Mean_GBI decreased significantly over time. Mean_BD and Mean_IBI increased and IBN decreased significantly only in the last 30 min. The blink features automatically detected in this study can be used to evaluate the progression of visual fatigue. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

13 pages, 1176 KiB

Open AccessArticle

Splitting the P-Wave: Improved Evaluation of Left Atrial Substrate Modification after Pulmonary Vein Isolation of Paroxysmal Atrial Fibrillation

by Aikaterini Vraka, Vicente Bertomeu-González, Fernando Hornero, Aurelio Quesada, Raúl Alcaraz and José J. Rieta

Sensors 2022, 22(1), 290; https://doi.org/10.3390/s22010290 - 31 Dec 2021

Cited by 3 | Viewed by 2318

Abstract

Atrial substrate modification after pulmonary vein isolation (PVI) of paroxysmal atrial fibrillation (pAF) can be assessed non-invasively by analyzing P-wave duration in the electrocardiogram (ECG). However, whether right (RA) and left atrium (LA) contribute equally to this phenomenon remains unknown. The present study splits fundamental P-wave features to investigate the different RA and LA contributions to P-wave duration. Recordings of 29 pAF patients undergoing first-ever PVI were acquired before and after PVI. P-wave features were calculated: P-wave duration (

P W D

), duration of the first (

P W D_{o n - p e a k}

) and second (

P W D_{p e a k - o f f}

) P-wave halves, estimating RA and LA conduction, respectively. P-wave onset (

P W_{o n} - R

) or offset (

P W_{o f f} - R

) to R-peak interval, measuring combined atrial/atrioventricular and single atrioventricular conduction, respectively. Heart-rate fluctuation was corrected by scaling. Pre- and post-PVI results were compared with Mann–Whitney U-test.

P W D

was correlated with the remaining features. Only

P W D

(non-scaling:

Δ = - 9.84 %

p = 0.0085

, scaling:

Δ = - 17.96 %

p = 0.0442

) and

P W D_{p e a k - o f f}

(non-scaling:

Δ = - 22.03 %

p = 0.0250

, scaling:

Δ = - 27.77 %

p = 0.0268

) were decreased. Correlation of all features with

P W D

was significant before/after PVI (

p < 0.0001

), showing the highest value between

P W D

and

P W_{o n} - R

(

ρ_{m a x} = 0.855

P W D

correlated more with

P W D_{o n - p e a k}

(

ρ =

0.540–0.805) than

P W D_{p e a k - o f f}

(

ρ =

0.419–0.710).

P W D

shortening after PVI of pAF stems mainly from the second half of the P-wave. Therefore, noninvasive estimation of LA conduction time is critical for the study of atrial substrate modification after PVI and should be addressed by splitting the P-wave in order to achieve improved estimations. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

13 pages, 1394 KiB

Open AccessArticle

Differences in the Asymmetry of Beat-to-Beat Fetal Heart Rate Accelerations and Decelerations at Preterm and Term Active Labor

by Carolina López-Justo, Adriana Cristina Pliego-Carrillo, Claudia Ivette Ledesma-Ramírez, Hugo Mendieta-Zerón, Miguel Ángel Peña-Castillo, Juan Carlos Echeverría, Jorge Rodríguez-Arce and José Javier Reyes-Lagos

Sensors 2021, 21(24), 8249; https://doi.org/10.3390/s21248249 - 10 Dec 2021

Cited by 5 | Viewed by 3209

Abstract

The fetal autonomic nervous system responds to uterine contractions during active labor as identified by changes in the accelerations and decelerations of fetal heart rate (FHR). Thus, this exploratory study aimed to characterize the asymmetry differences of beat-to-beat FHR accelerations and decelerations in preterm and term fetuses during active labor. In an observational study, we analyzed 10 min of fetal R-R series collected from women during active preterm labor (32–36 weeks of pregnancy, n = 17) and active term labor (38–40 weeks of pregnancy, n = 27). These data were used to calculate the Deceleration Reserve (DR), which is a novel parameter that quantifies the asymmetry of the average acceleration and deceleration capacity of the heart. In addition, relevant multiscale asymmetric indices of FHR were also computed. Lower values of DR, calculated with the input parameters of T = 50 and s = 10, were associated with labor occurring at the preterm condition (p = 0.0131). Multiscale asymmetry indices also confirmed significant (p < 0.05) differences in the asymmetry of FHR. Fetuses during moderate premature labor may experience more decaying R-R trends and a lower magnitude of decelerations compared to term fetuses. These differences of FHR dynamics might be related to the immaturity of the fetal cardiac autonomic nervous system as identified by this system response to the intense uterine activity at active labor. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Graphical abstract

18 pages, 1283 KiB

Open AccessArticle

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

by Félix Nieto-del-Amor, Raja Beskhani, Yiyao Ye-Lin, Javier Garcia-Casado, Alba Diaz-Martinez, Rogelio Monfort-Ortiz, Vicente Jose Diago-Almela, Dongmei Hao and Gema Prats-Boluda

Sensors 2021, 21(18), 6071; https://doi.org/10.3390/s21186071 - 10 Sep 2021

Cited by 14 | Viewed by 2158

Abstract

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

15 pages, 1328 KiB

Open AccessArticle

Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography

by Félix Nieto-del-Amor, Gema Prats-Boluda, Jose Luis Martinez-De-Juan, Alba Diaz-Martinez, Rogelio Monfort-Ortiz, Vicente Jose Diago-Almela and Yiyao Ye-Lin

Sensors 2021, 21(10), 3350; https://doi.org/10.3390/s21103350 - 12 May 2021

Cited by 14 | Viewed by 2272

Abstract

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

18 pages, 1765 KiB

Open AccessArticle

Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

by Gema Prats-Boluda, Julio Pastor-Tronch, Javier Garcia-Casado, Rogelio Monfort-Ortíz, Alfredo Perales Marín, Vicente Diago, Alba Roca Prats and Yiyao Ye-Lin

Sensors 2021, 21(7), 2496; https://doi.org/10.3390/s21072496 - 3 Apr 2021

Cited by 7 | Viewed by 4847

Abstract

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RF_{F1_2} and ELM_{F1_2} provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELM_{F1_2} outperformed RF_{F1_2} in sensitivity, being similar to those of ELM_Sens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

17 pages, 7531 KiB

Open AccessArticle

Characterization of Pelvic Floor Activity in Healthy Subjects and with Chronic Pelvic Pain: Diagnostic Potential of Surface Electromyography

by Monica Albaladejo-Belmonte, Marta Tarazona-Motes, Francisco J. Nohales-Alfonso, Maria De-Arriba, Jose Alberola-Rubio and Javier Garcia-Casado

Sensors 2021, 21(6), 2225; https://doi.org/10.3390/s21062225 - 23 Mar 2021

Cited by 9 | Viewed by 5792

Abstract

Chronic pelvic pain (CPP) is a highly disabling disorder in women usually associated with hypertonic dysfunction of the pelvic floor musculature (PFM). The literature on the subject is not conclusive about the diagnostic potential of surface electromyography (sEMG), which could be due to poor signal characterization. In this study, we characterized the PFM activity of three groups of 24 subjects each: CPP patients with deep dyspareunia associated with a myofascial syndrome (CPP group), healthy women over 35 and/or parous (>35/P group, i.e., CPP counterparts) and under 35 and nulliparous (<35&NP). sEMG signals of the right and left PFM were recorded during contractions and relaxations. The signals were characterized by their root mean square (RMS), median frequency (MDF), Dimitrov index (DI), sample entropy (SampEn), and cross-correlation (CC). The PFM activity showed a higher power (>RMS), a predominance of low-frequency components (<MDF, >DI), greater complexity (>SampEn) and lower synchronization on the same side (<CC) in CPP patients, with more significant differences in the >35/P group. The same trend in differences was found between healthy women (<35&NP vs. >35/P) associated with aging and parity. These results show that sEMG can reveal alterations in PFM electrophysiology and provide clinicians with objective information for CPP diagnosis. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Biosignal Sensing and Processing for Clinical Diagnosis

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (13 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI